Inertia constants for individual power plants David Kraljic COMCOM d.o.o.

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Inertia constants for individual power plants

David Kraljic

COMCOM d.o.o.,

Faculty of Electrical Engineering,

University of Ljubljana

Ljubljana, Slovenia

david.kraljic@comcom.si

Blaz Sobocan

Jernej Katanec

Matej Logar

Miha Troha

COMCOM d.o.o.

Ljubljana, Slovenia

Abstract—Keeping the power system stable is becoming more

challenging with the growing share of renewable energy sources

of low or negligible inertia. Inertia constants for individual

power plants are generally not known and are roughly estimated

by considering the type of power generation technology and

the nameplate capacity of plants. More accurate knowledge of

inertia constants of individual power plants would give greater

transparency to decisions of the transmission system operator

(TSO) and to auctions for the procurement of inertia services.

Additionally, a more accurate forecast or estimation of system

inertia would improve the price signals to the power market well

in advance of balancing actions taken by the TSO.

We develop methods based on a combination of mathematical

optimisation and machine learning that reverse-engineer the

inertia constants of individual power plants from the historical

values of their power production and from aggregate values of

inertia in a power system. We demonstrate the methods for the

power system of Great Britain (GB), where historical values

for aggregate inertia are published by the TSO. We show that

the recovered inertia data is crucial in understanding certain

individual balancing decisions by the TSO. We use the reverse-

engineered inertia constants to predict system inertia which gives

valuable information to the power market.

Index Terms—Optimisation, Power system dynamics, Power

system economics, Power system stability, Reverse engineering

I. INTRODUCTION

A satisfactory level of inertia in a power system is crucial

for its stability. Inertia is provided by large rotating masses of

conventional generators that are electro-mechanically coupled

to the grid and can therefore transfer rotational kinetic energy

to and from the grid. This has a stabilising effect on the rate

of change of frequency (ROCOF) (see e.g. the swing equation

[1]), which is crucial in the events of faults, unexpected dis-

connections and large changes in generation or consumption.

Most devices connected to the grid have automatic protection

systems that disconnect the device from the grid in case

of large ROCOF and thus low inertia is associated with an

increased risk of cascading failures [2].

Increasing penetration of renewable generation such as wind

and photovoltaics, which are not electro-mechanically coupled

to the grid, is causing low levels of inertia and increased

instability of the grid [3]. Therefore, besides increasing the

effective grid inertia [4], the estimation of grid inertia is crucial

in future-prooﬁng the grid.

The standard approach to measure inertia, as it was at some

point in time, is via frequency transients analysis (e.g. post

fault), where the speed and nadir of the frequency drop, or

other properties of the transient, are used to extract the amount

of grid inertia at the point of the fault [5]–[7]. The shortcoming

of this approach is that the estimation is done ‘ofﬂine’ and

only estimates the value of inertia at that moment of time

in the past. Since faults do not occur regularly this method

yields few samples for total system inertia. Another approach

is to focus on measuring or estimating the total inertia in

real-time (e.g. based on phasor measurement units and/or

wide area monitoring [8]–[10]). This approach is valuable to

TSOs as it informs operational decisions in real-time, however,

such estimates are less useful to the electricity market, where

activities usually cease well before real-time.

Instead, we focus in this contribution on the expected or

forecast inertia a few hours before the delivery start rather

than in real-time or ofﬂine. The expected or predicted value of

inertia for some settlement period a few hours before it starts

is important as it determines actions of the TSO that are taken

before real-time delivery in anticipation of low inertia. Early

inertia prediction can also provide signals to the electricity

market, so that mitigating actions can be already taken on

the market (and not just in real-time, when it can already be

too late, given that large synchronous generators take long to

start-up).

The need for procurement of inertia through market mech-

anisms [11] e.g. via periodic auctions has appeared because

low inertia states due to renewables are becoming more and

more frequent. In contrast to approaches in literature that focus

on the total system inertia, we estimate inertia values for

each individual power plant. For the transparency and better

competition in such markets, it is crucial that individual power

plant parameters are known. For example, currently, the Grid

Code of GB prevents the TSO to disclose the individual power

plant inertia parameters1.

Another area where the data on inertia values for individual

power plants would increase transparency is in understanding

the individual actions taken by the TSO. Speciﬁcally, the GB

TSO has a policy to keep grid inertia above 140 GVAs [12],

as we can see from Figs.1, 2. Therefore, if the predicted

aggregate inertia is below this trigger level and the market

1https://data.nationalgrideso.com/plans-reports-analysis/covid-19-

preparedness-materials/r/transparency forum inertia deep dive follow-

up q&a

arXiv:2210.03661v1 [eess.SY] 7 Oct 2022

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InertiaconstantsforindividualpowerplantsDavidKraljicCOMCOMd.o.o.,FacultyofElectricalEngineering,UniversityofLjubljanaLjubljana,Sloveniadavid.kraljic@comcom.siBlazSobocanJernejKatanecMatejLogarMihaTrohaCOMCOMd.o.o.Ljubljana,SloveniaAbstractKeepingthepowersystemstableisbecomingmorechallengingwiththeg...

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Inertia constants for individual power plants David Kraljic COMCOM d.o.o.

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